文献类型：会议论文

英文题名：An improved neural network model for graduate education evaluation

作者：Bao Qiong[1];Gu Xingsheng[1];Shen Yongjun[1]

机构：[1]E China Univ Sci & Technol, Res Inst Automat 1, Shanghai 200237, Peoples R China

会议论文集：International Conference on Computational Intelligence and Security

会议日期：DEC 15-19, 2007

会议地点：Harbin, PEOPLES R CHINA

语种：英文

外文关键词：education evaluation; PID neural network (PIDNN); variable integral; partial differential; multi-model

摘要：As an important part of higher education evaluation, graduate education evaluation plays a significant role to safeguard a sustainable development of higher education, especially graduate education. Considering the problems that traditional evaluation methods mainly depend on the experience of experts to a certain extent and there are many correlative factors which make it difficult to establish the evaluation model, a novel multiple improved PIDNN model is proposed in this paper to get the education evaluation model. In this model, the concepts of variable integral and partial differential are introduced into the design of hidden-layer of PIDNN, and the multiple improved PIDNN are dynamically combined to get the evaluation output with a gating network. The simulation results with the real data provided by East China University of Science an Technology indicate the validity of this modeling approach.